基于GWR模型的农业碳排放影响因素时空分异研究

Study on Spatial and Temporal Variation of Impact Factors in Agricultural Carbon Emissions Based on GWR Model

摘要:

从八大综合经济区的角度测算了我国31个省市农业碳排放和碳源贡献度，利用空间自相关性分析对农业碳排放的区域特征和空间集聚现象进行研究，同时运用地理加权回归模型分析了各影响因素在不同时间和空间层面对碳排放的影响程度。结果表明：（1）东南沿海、长江中游、大西南地区稻田碳排放占比最大；北部沿海、黄河中游地区农用物资与农田利用碳排放占比最大；大西北地区除宁夏、新疆外，动物养殖碳排放占比最大。（2）各省农业碳排放存在显著的空间正相关性。（3）农用机械总动力对碳排放具有正向影响，农业对外开放度、人均农业GDP对大部分省份碳排放具有负向影响，回归系数在空间上表现出明显的梯度分布。基于此提出了相应的对策建议。

关键词:地理加权回归;农业碳排放;空间自相关;影响因素;时空分异

Abstract:

This paper measures agricultural carbon emissions and?ratios of carbon sources of different provinces from?the perspective of eight major economic zones, then analyses regional characteristics and spatial agglomeration of agricultural carbon emissions by spatial autocorrelation analysis. Moreover,?the geographical weighted regression model is used to analyze the impact of various factors on carbon emissions at different time and space levels. Results show: (1) Rice fields account for the largest proportion of carbon emissions in the southeast coast, the middle reaches of the Yangtze river and the southwest regions. Agricultural materials and farmland account for the largest proportion of carbon emissions in the northern coast and the middle reaches of the Yellow River. In the northwest regions, except Ningxia and Xinjiang, animal breeding accounts for the largest proportion of carbon emissions.(2)There is a significant spatial positive correlation between provincial agricultural carbon emissions.(3)The total power of agricultural machinery has a positive impact on agricultural carbon emissions, agricultural openness and per capita agricultural GDP have a negative impact on agricultural carbon emissions for most provinces.The regression coefficients show an obvious gradient distribution in space. Finally, it puts forward some suggestions according to the empirical conclusions.

Key words:geographical weighted regression;agricultural carbon emissions;spatial autocorrelation;impact factors;spatial and temporal variation